{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Vegetation Attenuation\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some radar applications, the attenuation in vegetation can be important. However, there is a wide range of conditions and foliage types making it extremely difficult to develop generalized prediction methods.  Furthermore, there is a lack of experimental data collected in a single database for use.  However, models have been developed for extensive vegetation such as woodland for particular frequency ranges and different types of geometry.  \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the distances within the vegetation (m), the specific attenuation (dB/m) and the maximum attenuation (dB) using the `linspace` routine from `scipy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "distance = linspace(0., 100.0)\n",
    "\n",
    "specific_attenuation = 0.39\n",
    "\n",
    "maximum_attenuation = 34.10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the keyword args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    " kwargs = {'distance': distance,\n",
    "\n",
    "           'specific_attenuation': specific_attenuation,\n",
    "\n",
    "           'maximum_attenuation': maximum_attenuation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the total attenuation due to vegetation using the `vegetation` routines from `wave_propagation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.wave_propagation import vegetation\n",
    "\n",
    "attenuation = vegetation.attenuation(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the attenuation due to vegetation using the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Set up the axes\n",
    "\n",
    "fig, axes1 = plt.subplots()\n",
    "\n",
    "axes2 = axes1.twinx()\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "axes1.semilogy(distance, attenuation)\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "axes1.set_title('Vegetation Attenuation', size=14)\n",
    "\n",
    "axes1.set_xlabel('Distance (m)', size=12)\n",
    "\n",
    "axes1.set_ylabel('Attenuation (dB)', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "axes1.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "axes1.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
